Allocating data based on memory device performance in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) processing unit includes generating a request for mapping parameters for transmission a storage unit in a dispersed storage network (DSN) and receiving mapping parameter data in response. Namespace mapping data is generated, indicating a mapping of possible slice names to a plurality of memory devices of the storage unit by performing a first deterministic mapping function based on the mapping parameter data. Slice name subset data is generated, indicating a subset of the possible slice names based on a health status indicators corresponding to the plurality of memory devices. A slice name corresponding to a first encoded slice of a first data object is selected from the subset indicated by the slice name subset data. A first write request that includes the first encoded slice is generated for transmission via the network to the storage unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No.15/237,820, entitled “ALLOCATING DATA BASED ON MEMORY DEVICE PERFORMANCEIN A DISPERSED STORAGE NETWORK”, filed Aug. 16, 2016, which claimspriority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional ApplicationNo. 62/314,839, entitled “PROCESSING AN ENCODED DATA SLICE IN ADISPERSED STORAGE NETWORK”, filed Mar. 29, 2016, both of which arehereby incorporated herein by reference in their entirety and made partof the present U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 10 is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention; and

FIG. 11 is a logic diagram of an example of a method of allocating databased on memory device performance in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1−T), a data segment number (e.g., oneof 1−Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a computing device 16 of FIG. 1, thenetwork 24 of FIG. 1, and a plurality of storage units 1-n. Thecomputing device 16 can include the interface 32 of FIG. 1, thecomputing core 26 of FIG. 1, and the DS client module 34 of FIG. 1. Thecomputing device 16 can function as a dispersed storage processing agentfor computing device 14 as described previously, and may hereafter beinterchangeably referred to as a distributed storage and task (DST)processing unit. Each storage unit may be implemented utilizing thestorage unit 36 of FIG. 1, and can include a plurality of memory devices1-k. Each storage unit can include the same number or a different numberof memory devices. The DSN functions to allocate data based on memorydevice performance.

In various embodiments, a DST processing unit, when storing, listing,retrieving, or otherwise accessing slices from at least one storageunit, may benefit from knowledge of the status of memory devices withinthose storage units. In particular, the DST processing unit can benefitfrom knowledge of which memory devices of a storage unit areunderperforming, failing, impaired, being-rebuilt, etc. However, the DSTprocessing unit needs knowledge of which namespace ranges those memorydevices are responsible for within each storage unit to determine whichpossible slice names of encoded slices correspond to storage locationswithin those memory devices. With this knowledge, the DST processingunit may prefer to avoid sending access requests to those storage unitsthat have an impaired memory device responsible for storage of the dataslice in question. The information regarding which memory devices areresponsible for which namespace ranges can be small when each memorydevice is responsible for only a single fixed range of the namespace,and this information can be communicated efficiently to the DSTprocessing unit as a response to a “memory device map request.” However,certain mapping strategies may result in a very large number ofnamespace ranges being assigned to each memory device. This may occur,for example, when a storage unit uses a Decentralized Agreement Protocol(DAP) depicted in FIG. 11 to map potentially thousands of sub-ranges (ormore), to individual memory devices in accordance with the capacity ofeach memory device. The advantage of this strategy is that when anymemory device fails or gives up its assigned namespace ranges, the manyhundreds or thousands of sub-ranges can be more or less equallyredistributed across all surviving memory devices, rather than just toneighbors (which is required when keeping the namespace range to memorydevice ration 1:1). However, the many disks, and their thousands ofsub-ranges represents a large degree of information, which isinefficient to communicate as a response to a “memory device maprequest”.

As an alternative, a DST processing unit, rebuild module, or otherrequester can send a “mapping parameters request” to the storage unit.The storage unit can then return the parameters it uses to generate itsown namespace range to memory device map. These mapping parameters caninclude memory device capacities, memory device identifiers, analgorithm name (e.g. what Decentralized Agreement Protocol deterministicfunction to use), the number of namespace range subdivisions. From thisinformation, the DST processing unit can re-execute the same algorithmthe storage unit executed to derive its mapping of namespace ranges tomemory devices, and thereby know which slice names are mapped to whichmemory devices. Furthermore, with the health information of the memorydevices, the DST processing unit can know which slice names to avoid(due to potentially missing or suffering performance issues), rebuildmodules can more efficiently focus on scanning ranges impacted by afailing memory device, and/or orchestration processes (such as anupgrade process which must ensure vault health) can avoid simultaneouslybringing down combinations of ds units which would result in rangesbeing inaccessible for reads or writes (due to internal memory deviceerrors on ds units not brought down by the orchestration process).

The DST processing unit can receive these health statuses via systemregistry information and/or a lookup table, and/or can receive thehealth statuses from the corresponding storage unit(s). In variousembodiments, the health statuses can be received along with the mappingparameters. In other embodiments, the health statuses can be received ata predetermined time interval. The health status can include raw data orbe a calculated value based on one or more factors, such as performance,storage limits, past history of accesses, unresponsiveness, latency,device capabilities, memory and/or errors, etc. In various embodiments,the health status of each memory device of a storage unit can becompared to a health status threshold, and a set of slice name subsetscorresponding to one or more ranges of the namespace corresponding tomemory devices with health statuses that compare favorably to the healthstatus threshold can be determined. In various embodiments, when writinga slice of a data object, the slice name of the data object can beselected from this set of slice name subsets. In various embodiments,generating a set of encoded slices of a data object will be based on theset of slice name subsets across all of the corresponding storage unitsthat the encoded slices will be distributed amongst. For example, theencoding function used to generate the encoded slices can take thisinformation as a parameter, and as a result will only generate sliceswith slice name subsets.

In various embodiments, this method of reverse-engineering the namespaceof a storage unit can be used to reallocate data in a single storageunit, or to reallocate data amongst multiple storage units. For example,the DST processing unit may request the mapping parameters and/or healthstatuses of memory devices from one or more storage units in response toa reallocation requirement, determine which slices are stored inlow-health memory devices of one or more storage units, and reallocatethem accordingly. In various embodiments, the slices to be reallocatedcan be based on a health status threshold as discussed previously, andslices of memory devices performing below the health status thresholdwill be reallocated. In various devices, a data object must have atleast a fixed percentage of slices stored in “healthy” memory devices,and only encoded slices of data objects that fail to meet thisrequirement will be reallocated. In various embodiments, a reallocationmaximum and/or minimum can be specified. For example, only a number ofslices up to the reallocation maximum can be reallocated, and the slicesto be reallocated can be selected based on comparing health statuslevels of various memory devices, targeting encoded slices of dataobjects with several slices in low-health memory devices, targetingstorage units with several low-health memory devices, etc. In various atleast a minimum number of slices can be reallocated, for example, basedon similar factors. In various embodiments, the reallocation requirementwill be received via the network, or can be determined by the DSTprocessing unit itself, for example based on changes in memory devicehealth, based on past accesses, and/or based on a failed access attempt.In various embodiments, the reallocation requirement can be for a singleslice, a slices of a single data object, slices in a single memorydevice, slices across a single storage unit, or across several of theseelements in a system. For example, the reallocation requirement can bedetermined in response to an access request. As part of performing theaccess, the DST processing unit can rebuild the namespace, evaluate thehealth status of the corresponding memory device, and determine if theslice in question needs to be moved to a new location. In variousembodiments, upon determining that a slice needs to be moved, as part ofgenerating a new slice name, the entire set of encoded slices of thedata object is regenerated and are moved to new locations accordingly.In other embodiments, in response to identifying the low-health memorydevice during the access request, the reallocation requirement caninclude moving one, multiple, or all of the additional slices stored inthe low-health memory device to new locations. In various embodiments,the DSP processing unit can generate notification to other elements ofthe DSN and/or a user of the DSN indicating low-health memory devices tobe replaced.

In various embodiments, a processing system of a dispersed storage andtask (DST) processing unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to generate a first requestfor mapping parameters for transmission via a network to a first one ofa plurality of storage units in a dispersed storage network (DSN) and toreceive first mapping parameter data in response. First namespacemapping data that indicates a mapping of a plurality of possible slicenames to a first plurality of memory devices of the first one of theplurality of storage units is generated by performing a firstdeterministic mapping function based on the first mapping parameterdata. Slice name subset data indicating a subset of the plurality ofpossible slice names is generated based on a plurality of health statusindicators corresponding to the first plurality of memory devices. Afirst slice name corresponding to a first encoded slice of a first dataobject is selected from the subset indicated by the slice name subsetdata. A first write request is generated for transmission via thenetwork that includes the first encoded slice to the first one of theplurality of storage units.

In various embodiments, memory device status data is received from theone of the plurality of storage units via the network that includes theplurality of health status indicators. In various embodiments, thememory device status data is received from the one of the plurality ofstorage units in response to the first request for mapping parameters.In various embodiments, generating the slice name subset data includescomparing each of the plurality of health status indicators to a healthstatus threshold, and wherein a one of the plurality of possible slicenames is included in the subset when its corresponding health statusindicator compares favorably to the health status threshold.

In various embodiments, the first deterministic mapping function isbased on a Decentralized Agreement Protocol (DAP). In variousembodiments, the first deterministic mapping function corresponds to amapping algorithm utilized by the one of the plurality of storage units,and wherein the first encoded slice is stored in a one of the firstplurality of memory devices of the one of the plurality of storage unitsbased on performing the mapping algorithm on the first encoded slice. Invarious embodiments, the first deterministic mapping function is one ofa plurality of deterministic mapping functions indicated by the firstmapping parameter data.

In various embodiments, a plurality of selected slice names thatincludes the first slice name are selected, each corresponding to eachof a plurality of encoded slices corresponding to the first data objectbased on the slice name subset data. A plurality of write requests thatincludes the first write request are generated, each for transmissionvia the network to a corresponding one of the plurality of storageunits, wherein each of the plurality of write requests indicates acorresponding one of the plurality of encoded slices. The first requestfor mapping parameters is transmitted to the plurality of storage unitsand each of a plurality of mapping parameter data, which includes thefirst mapping parameter data, is received from each of the plurality ofstorage units. The first namespace mapping data indicates, for each ofthe plurality of storage units, the corresponding mapping of a pluralityof possible slice names to the first plurality of memory devices of theeach of the plurality of storage units. The slice name subset dataindicates a plurality of subsets of the plurality of possible slicenames for each of the plurality of storage units based on the pluralityof health status indicators corresponding to the first plurality ofmemory devices corresponding to each of the plurality of storage units.In various embodiments, an encoding function is performed on the firstdata object based on the slice name subset data to generate theplurality of encoded slices. The corresponding plurality of selectedslice names are also generated by the encoding function, and the each ofthe corresponding plurality of selected slice names are included in oneof the plurality of subsets indicated by the slice name subset data.

In various embodiments, an access request is received from a requestingentity via the network indicating a second encoded slice stored in theDSN. A second request for mapping parameters are generated fortransmission via a network to a second one of a plurality of storageunits in a dispersed storage network (DSN) and receiving second mappingparameter data in response. Slice location data is generated thatindicates a one of a second plurality of memory devices of the secondone of the plurality of storage units corresponding to a location wherethe second encoded slice is stored by performing a second deterministicmapping function on a second slice name corresponding to the secondencoded slice based on the second mapping parameter data. The accessrequest is performed when a health status indicator corresponding to thesecond one of the second plurality of memory devices compares favorablyto a health status threshold. In various embodiments, a low-healthnotification is generated for transmission to the requesting entity viathe network indicating that the access request will not be performedwhen the health status indicator corresponding to the second one of thesecond plurality of memory devices compares unfavorably to the healthstatus threshold. The DST processing unit foregoes the access request inthis case.

In various embodiments, the first request for mapping parameters isgenerated in response to a slice reallocation requirement. Reallocationdata is generated in response to the slice name subset data indicatingthat an original slice name of the first encoded slice is not includedin the subset. The reallocation data indicates a new slice name toreplace the original slice name of the first encoded slice. The firstwrite request indicates the original slice name and a request to removethe first encoded slice from a memory device corresponding to old slicename. In various embodiments, the slice reallocation requirement isreceived via the network. In various embodiments, the slice reallocationrequirement is based on a predetermined time interval elapsing. Invarious embodiments, the slice reallocation requirement is based onperformance data from past access to the plurality of storage units. Invarious embodiments, a request for memory device status data from thefirst one of the plurality of storage units is transmitted via thenetwork in response to the slice reallocation requirement. A pluralityof health status indicators are received via the network in response tothe request for memory device status data.

In various embodiments, reallocation data is generated in response tothe size of the subset indicated by the slice name subset data comparingunfavorably to a required healthy memory device count. The reallocationdata indicates a plurality of encoded slices stored in the one of theplurality of storage units, each with a corresponding original slicename that is not included in the subset indicated by the slice namesubset data. The reallocation data further indicates a plurality of newslice names, each included in the subset indicated by the slice namesubset data, and each of the plurality of encoded slices is assigned acorresponding one of the plurality of new slice names. A secondplurality of write requests are generated, each for transmission via thenetwork to the plurality of storage units. Each of the plurality ofwrite requests indicates that a corresponding one of the plurality ofencoded slices be moved from an old one of the first plurality of memorydevices corresponding to the original slice name to a new one of thefirst plurality of memory devices corresponding to the new slice name.In various embodiments, each of the plurality of new slice names aredetermined based on performing an encoding function on one of aplurality of data objects of the corresponding one of the plurality ofencoded slices based on the slice name subset data.

FIG. 10 is a schematic block diagram of an embodiment of a decentralizedagreement module 350 that includes a set of deterministic functions 1-N,a set of normalizing functions 1-N, a set of scoring functions 1-N, anda ranking function 352. Each of the deterministic function, thenormalizing function, the scoring function, and the ranking function352, may be implemented utilizing the computing core 26 of FIG. 2. Thedecentralized agreement module 350 may be implemented utilizing anymodule and/or unit of a dispersed storage network (DSN). For example,the decentralized agreement module is implemented utilizing thedistributed storage and task (DST) client module 34 of FIG. 1. Invarious embodiments, data will be allocated amongst memory devices of astorage unit as described previously by utilizing the decentralizedagreement module.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSclient module 34 of FIG. 1, a computing device 12-16 of FIG. 1, anintegrity unit 20 of FIG. 1, and a managing unit 18 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc., the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 11 is a flowchart illustrating an example of allocating data basedon memory device performance. In particular, a method is presented foruse in association with one or more functions and features described inconjunction with FIGS. 1-10, for execution by a dispersed storage andtask (DST) processing unit that includes a processor or via anotherprocessing system of a dispersed storage network that includes at leastone processor and memory that stores instruction that configure theprocessor or processors to perform the steps described below. Step 1102includes generating a first request for mapping parameters fortransmission via a network to a first one of a plurality of storageunits in a dispersed storage network (DSN) and receiving first mappingparameter data in response. Step 1104 includes generating firstnamespace mapping data that indicates a mapping of a plurality ofpossible slice names to a first plurality of memory devices of the firstone of the plurality of storage units by performing a firstdeterministic mapping function based on the first mapping parameterdata. Step 1106 includes generating slice name subset data indicating asubset of the plurality of possible slice names based on a plurality ofhealth status indicators corresponding to the first plurality of memorydevices. Step 1108 includes selecting a first slice name correspondingto a first encoded slice of a first data object from the subsetindicated by the slice name subset data. Step 1110 includes generating afirst write request for transmission via the network that includes thefirst encoded slice to the first one of the plurality of storage units.

In various embodiments, memory device status data is received from theone of the plurality of storage units via the network that includes theplurality of health status indicators. In various embodiments, thememory device status data is received from the one of the plurality ofstorage units in response to the first request for mapping parameters.In various embodiments, generating the slice name subset data includescomparing each of the plurality of health status indicators to a healthstatus threshold, and wherein a one of the plurality of possible slicenames is included in the subset when its corresponding health statusindicator compares favorably to the health status threshold.

In various embodiments, the first deterministic mapping function isbased on a Decentralized Agreement Protocol (DAP). In variousembodiments, the first deterministic mapping function corresponds to amapping algorithm utilized by the one of the plurality of storage units,and wherein the first encoded slice is stored in a one of the firstplurality of memory devices of the one of the plurality of storage unitsbased on performing the mapping algorithm on the first encoded slice. Invarious embodiments, the first deterministic mapping function is one ofa plurality of deterministic mapping functions indicated by the firstmapping parameter data.

In various embodiments, a plurality of selected slice names thatincludes the first slice name are selected, each corresponding to eachof a plurality of encoded slices corresponding to the first data objectbased on the slice name subset data. A plurality of write requests thatincludes the first write request are generated, each for transmissionvia the network to a corresponding one of the plurality of storageunits, wherein each of the plurality of write requests indicates acorresponding one of the plurality of encoded slices. The first requestfor mapping parameters is transmitted to the plurality of storage unitsand each of a plurality of mapping parameter data, which includes thefirst mapping parameter data, is received from each of the plurality ofstorage units. The first namespace mapping data indicates, for each ofthe plurality of storage units, the corresponding mapping of a pluralityof possible slice names to the first plurality of memory devices of theeach of the plurality of storage units. The slice name subset dataindicates a plurality of subsets of the plurality of possible slicenames for each of the plurality of storage units based on the pluralityof health status indicators corresponding to the first plurality ofmemory devices corresponding to each of the plurality of storage units.In various embodiments, an encoding function is performed on the firstdata object based on the slice name subset data to generate theplurality of encoded slices. The corresponding plurality of selectedslice names are also generated by the encoding function, and the each ofthe corresponding plurality of selected slice names are included in oneof the plurality of subsets indicated by the slice name subset data.

In various embodiments, an access request is received from a requestingentity via the network indicating a second encoded slice stored in theDSN. A second request for mapping parameters are generated fortransmission via a network to a second one of a plurality of storageunits in a dispersed storage network (DSN) and receiving second mappingparameter data in response. Slice location data is generated thatindicates a one of a second plurality of memory devices of the secondone of the plurality of storage units corresponding to a location wherethe second encoded slice is stored by performing a second deterministicmapping function on a second slice name corresponding to the secondencoded slice based on the second mapping parameter data. The accessrequest is performed when a health status indicator corresponding to thesecond one of the second plurality of memory devices compares favorablyto a health status threshold. In various embodiments, a low-healthnotification is generated for transmission to the requesting entity viathe network indicating that the access request will not be performedwhen the health status indicator corresponding to the second one of thesecond plurality of memory devices compares unfavorably to the healthstatus threshold. The DST processing unit foregoes the access request inthis case.

In various embodiments, the first request for mapping parameters isgenerated in response to a slice reallocation requirement. Reallocationdata is generated in response to the slice name subset data indicatingthat an original slice name of the first encoded slice is not includedin the subset. The reallocation data indicates a new slice name toreplace the original slice name of the first encoded slice. The firstwrite request indicates the original slice name and a request to removethe first encoded slice from a memory device corresponding to old slicename. In various embodiments, the slice reallocation requirement isreceived via the network. In various embodiments, the slice reallocationrequirement is based on a predetermined time interval elapsing. Invarious embodiments, the slice reallocation requirement is based onperformance data from past access to the plurality of storage units. Invarious embodiments, a request for memory device status data from thefirst one of the plurality of storage units is transmitted via thenetwork in response to the slice reallocation requirement. A pluralityof health status indicators are received via the network in response tothe request for memory device status data.

In various embodiments, reallocation data is generated in response tothe size of the subset indicated by the slice name subset data comparingunfavorably to a required healthy memory device count. The reallocationdata indicates a plurality of encoded slices stored in the one of theplurality of storage units, each with a corresponding original slicename that is not included in the subset indicated by the slice namesubset data. The reallocation data further indicates a plurality of newslice names, each included in the subset indicated by the slice namesubset data, and each of the plurality of encoded slices is assigned acorresponding one of the plurality of new slice names. A secondplurality of write requests are generated, each for transmission via thenetwork to the plurality of storage units. Each of the plurality ofwrite requests indicates that a corresponding one of the plurality ofencoded slices be moved from an old one of the first plurality of memorydevices corresponding to the original slice name to a new one of thefirst plurality of memory devices corresponding to the new slice name.In various embodiments, each of the plurality of new slice names aredetermined based on performing an encoding function on one of aplurality of data objects of the corresponding one of the plurality ofencoded slices based on the slice name subset data.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to generate a first request for mapping parameters fortransmission via a network to a first one of a plurality of storageunits in the DSN and to receive a first mapping parameter data inresponse. First namespace mapping data that indicates a mapping of aplurality of possible slice names to a first plurality of memory devicesof the first one of the plurality of storage units is generated byperforming a first deterministic mapping function based on the firstmapping parameter data. Slice name subset data indicating a subset ofthe plurality of possible slice names is generated based on a pluralityof health status indicators corresponding to the first plurality ofmemory devices. A first slice name corresponding to a first encodedslice of a first data object is selected from the subset indicated bythe slice name subset data. A first write request is generated fortransmission via the network that includes the first encoded slice tothe first one of the plurality of storage units.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device thatincludes a processor, the method comprises: receiving first mappingparameter data from a first one of a plurality of storage units in adispersed storage network (DSN); generating first namespace mapping datathat indicates a mapping of a plurality of possible slice names to afirst plurality of memory devices of the first one of the plurality ofstorage units by performing a first deterministic mapping function basedon the first mapping parameter data; generating slice name subset dataindicating a subset of the plurality of possible slice names based on aplurality of health status indicators corresponding to the firstplurality of memory devices; selecting a first slice name correspondingto a first encoded slice of a first data object from the subsetindicated by the slice name subset data; and generating a first writerequest for transmission via the network that includes the first encodedslice to the first one of the plurality of storage units.
 2. The methodof claim 1, further comprising receiving memory device status data fromthe one of the plurality of storage units via the network that includesthe plurality of health status indicators.
 3. The method of claim 2,wherein the memory device status data is received from the one of theplurality of storage units in response to a first request for mappingparameters.
 4. The method of claim 1, wherein generating the slice namesubset data includes comparing each of the plurality of health statusindicators to a health status threshold, and wherein a one of theplurality of possible slice names is included in the subset when itscorresponding health status indicator compares favorably to the healthstatus threshold.
 5. The method of claim 1, wherein the firstdeterministic mapping function is based on a Decentralized AgreementProtocol (DAP).
 6. The method of claim 1, wherein the firstdeterministic mapping function corresponds to a mapping algorithmutilized by the one of the plurality of storage units, and wherein thefirst encoded slice is stored in a one of the first plurality of memorydevices of the one of the plurality of storage units based on performingthe mapping algorithm on the first encoded slice.
 7. The method of claim1, further comprising: selecting a plurality of selected slice namesthat includes the first slice name, each corresponding to each of aplurality of encoded slices corresponding to the first data object basedon the slice name subset data; and generating a plurality of writerequests that includes the first write request, each for transmissionvia the network to a corresponding one of the plurality of storageunits, wherein each of the plurality of write requests indicates acorresponding one of the plurality of encoded slices; wherein a firstrequest for mapping parameters is transmitted to the plurality ofstorage units and each of a plurality of mapping parameter data, whichincludes the first mapping parameter data, is received from each of theplurality of storage units; wherein the first namespace mapping dataindicates, for each of the plurality of storage units, the correspondingmapping of a plurality of possible slice names to the first plurality ofmemory devices of the each of the plurality of storage units; andwherein the slice name subset data indicates a plurality of subsets ofthe plurality of possible slice names for each of the plurality ofstorage units based on the plurality of health status indicatorscorresponding to the first plurality of memory devices corresponding toeach of the plurality of storage units.
 8. The method of claim 7,further comprising performing an encoding function on the first dataobject based on the slice name subset data to generate the plurality ofencoded slices, wherein the corresponding plurality of selected slicenames are also generated by the encoding function, and wherein the eachof the corresponding plurality of selected slice names are included inone of the plurality of subsets indicated by the slice name subset data.9. The method of claim 1, wherein the first deterministic mappingfunction is one of a plurality of deterministic mapping functionsindicated by the first mapping parameter data.
 10. The method of claim1, further comprising: receiving an access request from a requestingentity via the network indicating a second encoded slice stored in theDSN; generating a second request for mapping parameters for transmissionvia a network to a second one of a plurality of storage units in the DSNand receiving second mapping parameter data in response; generatingslice location data that indicates a one of a second plurality of memorydevices of the second one of the plurality of storage unitscorresponding to a location where the second encoded slice is stored byperforming a second deterministic mapping function on a second slicename corresponding to the second encoded slice based on the secondmapping parameter data; and performing the access request when a healthstatus indicator corresponding to the second one of the second pluralityof memory devices compares favorably to a health status threshold. 11.The method of claim 10, further comprising: generating a low-healthnotification for transmission to the requesting entity via the networkindicating that the access request will not be performed and foregoingthe access request when the health status indicator corresponding to thesecond one of the second plurality of memory devices comparesunfavorably to the health status threshold.
 12. The method of claim 1,wherein a first request for mapping parameters is generated in responseto a slice reallocation requirement, and the method further comprises:generating reallocation data in response to the slice name subset dataindicating that an original slice name of the first encoded slice is notincluded in the subset, wherein the reallocation data indicates a newslice name to replace the original slice name of the first encodedslice; wherein the first write request indicates the original slice nameand a request to remove the first encoded slice from a memory devicecorresponding to old slice name.
 13. The method of claim 12, wherein theslice reallocation requirement is received via the network.
 14. Themethod of claim 12, wherein the slice reallocation requirement is basedon a predetermined time interval elapsing.
 15. The method of claim 12,wherein the slice reallocation requirement is based on performance datafrom past access to the plurality of storage units.
 16. The method ofclaim 12, further comprising: transmitting a request for memory devicestatus data from the first one of the plurality of storage units via thenetwork in response to the slice reallocation requirement; and receivingplurality of health status indicators via the network in response to therequest for memory device status data.
 17. The method of claim 1,further comprising: generating reallocation data in response to the sizeof the subset indicated by the slice name subset data comparingunfavorably to a required healthy memory device count, wherein thereallocation data indicates a plurality of encoded slices stored in theone of the plurality of storage units, each with a correspondingoriginal slice name that is not included in the subset indicated by theslice name subset data, wherein the reallocation data further indicatesa plurality of new slice names, each included in the subset indicated bythe slice name subset data, and wherein each of the plurality of encodedslices is assigned a corresponding one of the plurality of new slicenames; and generating a second plurality of write requests, each fortransmission via the network to the plurality of storage units, whereineach of the plurality of write requests indicates that a correspondingone of the plurality of encoded slices be moved from an old one of thefirst plurality of memory devices corresponding to the original slicename to a new one of the first plurality of memory devices correspondingto the new slice name.
 18. The method of claim 17, wherein each of theplurality of new slice names are determined based on performing anencoding function on one of a plurality of data objects of thecorresponding one of the plurality of encoded slices based on the slicename subset data.
 19. A processing system of a computing devicecomprises: at least one processor; a memory that stores operationalinstructions, that when executed by the at least one processor cause theprocessing system to: receive first mapping parameter data from a firstone of a plurality of storage units in a dispersed storage network(DSN); generate first namespace mapping data that indicates a mapping ofa plurality of possible slice names to a first plurality of memorydevices of the first one of the plurality of storage units by performinga first deterministic mapping function based on the first mappingparameter data; generate slice name subset data indicating a subset ofthe plurality of possible slice names based on a plurality of healthstatus indicators corresponding to the first plurality of memorydevices; select a first slice name corresponding to a first encodedslice of a first data object from the subset indicated by the slice namesubset data; and generate a first write request for transmission via thenetwork that includes the first encoded slice to the first one of theplurality of storage units.
 20. A non-transitory computer readablestorage medium comprises: at least one memory section that storesoperational instructions that, when executed by a processing system of adispersed storage network (DSN) that includes a processor and a memory,causes the processing system to: receive first mapping parameter datafrom a first one of a plurality of storage units in a dispersed storagenetwork (DSN); generate first namespace mapping data that indicates amapping of a plurality of possible slice names to a first plurality ofmemory devices of the first one of the plurality of storage units byperforming a first deterministic mapping function based on the firstmapping parameter data; generate slice name subset data indicating asubset of the plurality of possible slice names based on a plurality ofhealth status indicators corresponding to the first plurality of memorydevices; select a first slice name corresponding to a first encodedslice of a first data object from the subset indicated by the slice namesubset data; and generate a first write request for transmission via thenetwork that includes the first encoded slice to the first one of theplurality of storage units.